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This paper introduces Opal (Opportunity-aware Policy Authorization for Laboratories), a framework that certifies whether adaptive experimentation should be enabled by precommitting to non-trivial adaptation, controlled target risk, and positive executed value after cost. It establishes an impossibility boundary and demonstrates the method on a Cell Painting dataset, achieving risk control and positive value.
This paper presents a two-step method for optimizing resource utilization in autonomous laboratories using constraint programming for scheduling and status dependencies for robust execution, demonstrated on a platform for metal-organic framework synthesis.